Fluid-based engineering systems are widely used and may include gas turbine engines for aviation and power generation, HVAC&R (heating, ventilation, air-conditioning and refrigeration), fuel cells, and other, more generalized fluid processing systems for hydrocarbon extraction, materials processing, and manufacture. These systems may contain any or all of the following components: turbo-machinery, fuel cell stacks, electric motors, pipes, ducts, valves, mixers, nozzles, heat exchangers, gears, chemical apparatuses and other devices for generating or modifying a fluid flow.
Each of these applications places different operational demands on the engineering control system. In gas turbine engine applications, for example, the relevant cycle is typically a Brayton turbine or first Ericsson cycle, and the basic thermodynamic parameters (or process variables) are the pressure, temperature and flow rate of the working fluid at the inlet, compressor, combustor, turbine, and exhaust. The parameters may be related to the overall thrust, rotational energy, or other measure of power output. In order to precisely control this output while maintaining safe, reliable and efficient engine operation, the engineering control system must be fast, accurate, robust, and provide real-time control capability across all required performance levels. While the relevant process variables vary depending on the system type and configuration, the need for precise, efficient and reliable engineering control remains the same, as do the economic constraints on overall cost and operational/maintenance requirements.
Further, because direct measurements of system parameters controlled may not be possible (due to undeveloped technology, prohibitive cost, unreliable equipment, etc.), the control system may require real time estimation of system parameters. System parameters may be mathematical abstractions of engineering systems and/or process for a given set of measured inputs used as control feedback.
In the past, control systems for such fluid-based engineering systems relied on piecewise linear state variable representations. These control systems, by their nature, were limited to relatively simple non-linear systems. Another approach used in the past relies on semi-empirical relationships that tie important system parameters to control sensors; the drawback of such a system is that it may lack accuracy and is expensive due to the additional hardware required for implementation. Other attempts have been made to deploy stationary simulations in a retail environment; however, by their nature, these models are large, use iterative solvers, have high maintenance cost and lack robustness critical in a real time environment.
A known approach to modern fluid-based engineering system control is the use of component level physics based non-iterative mathematical abstractions of fluid-based engineering systems. These mathematical abstractions are conceptualized in a software environment specific to the applied fluid-based engineering system. Such example systems and methods for engineering system control are further detailed in U.S. Pat. No. 8,090,456 (“System and method for design and control of engineering systems utilizing component-level dynamic mathematical model”), which is hereby incorporated by reference.
In such control systems, the system may experience computational inaccuracies when the fluid based engineering system is starting operation from a rested state. Starting conditions may have a detrimental effect on performance of a control system. As such, a need exists for an engine parameter on-board synthesis (EPOS) in the real-time control system of a fluid based engineering system that may overcome the computational inefficiencies of prior EPOS models.